Morbidity and Mortality After Surgery for Nonmalignant Colorectal Polyps: A 10-Year Nationwide Analysis
Bibliographic record
Abstract
OBJECTIVES: Rates of surgery for nonmalignant colorectal polyps are increasing in the United States despite evidence that most polyps can be managed endoscopically. We aimed to determine nationally representative estimates and to identify predictors of in-hospital mortality and morbidity after surgery for nonmalignant colorectal polyps. METHODS: Data were analyzed from the National Inpatient Sample for 2005-2014. All discharges for adult patients undergoing surgery for nonmalignant colorectal polyps were identified. Rates of in-hospital mortality and postoperative wound, infectious, urinary, pulmonary, gastrointestinal, or cardiovascular adverse events were calculated. Multivariable logistic regression using survey-weighted data was used to evaluate covariables associated with postoperative mortality and morbidity. RESULTS: An estimated 262,843 surgeries for nonmalignant colorectal polyps were analyzed. In-hospital mortality was 0.8% [95% confidence interval: 0.7%-0.9%] and morbidity was 25.3% [95% confidence interval: 24.2%-26.4%]. Postoperative mortality was associated with open surgical technique (vs laparoscopic), older age, black race (vs non-Hispanic white), Medicaid use, and burden of comorbidities. Female sex and private insurance were associated with lower risk. Patients developing a postoperative adverse event had a 106% increase in mean hospital length of stay (10.3 vs 5.0 days; P < 0.0001) and 91% increase in mean hospitalization cost ($77,015.24 vs $40,258.30; P < 0.0001). DISCUSSION: Surgery for nonmalignant colorectal polyps is associated with almost 1% mortality and common morbidity. These findings should inform risk vs benefit discussions for clinicians and patients, and although confounding by patient selection cannot be excluded, the risks associated with surgery support consideration of endoscopic resection as a potentially less invasive therapeutic option.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".